# app.py

# MIT License
# 
# Copyright (c) 2024 englissi
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# SOFTWARE.


import os
from transformers import T5Tokenizer, T5ForConditionalGeneration
import gradio as gr
from nltk.tokenize import sent_tokenize
from difflib import SequenceMatcher

# Ensure the necessary NLTK data is downloaded
os.system('python download.py')

# Load a pre-trained T5 model specifically fine-tuned for grammar correction
tokenizer = T5Tokenizer.from_pretrained("prithivida/grammar_error_correcter_v1")
model = T5ForConditionalGeneration.from_pretrained("prithivida/grammar_error_correcter_v1")

# Function to perform grammar correction
def grammar_check(text):
    sentences = sent_tokenize(text)
    corrected_sentences = []

    for sentence in sentences:
        input_text = f"gec: {sentence}"
        input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
        outputs = model.generate(input_ids, max_length=512, num_beams=4, early_stopping=True)
        corrected_sentence = tokenizer.decode(outputs[0], skip_special_tokens=True)
        corrected_sentences.append(corrected_sentence)

    # Function to underline and color revised parts
    def underline_and_color_revisions(original, corrected):
        diff = SequenceMatcher(None, original.split(), corrected.split())
        result = []
        for tag, i1, i2, j1, j2 in diff.get_opcodes():
            if tag == 'insert':
                result.append(f"<u style='color:red;'>{' '.join(corrected.split()[j1:j2])}</u>")
            elif tag == 'replace':
                result.append(f"<u style='color:red;'>{' '.join(corrected.split()[j1:j2])}</u>")
            elif tag == 'equal':
                result.append(' '.join(original.split()[i1:i2]))
        return " ".join(result)

    corrected_text = " ".join(
        underline_and_color_revisions(orig, corr) for orig, corr in zip(sentences, corrected_sentences)
    )

    return corrected_text

# Create Gradio interface with a writing prompt
interface = gr.Interface(
    fn=grammar_check,
    inputs="text",
    outputs="html",  # Output type is HTML
    title="Grammar Checker",
    description=(
        "Enter text to check for grammar mistakes.\n\n"
        "Writing Prompt:\n"
        "In the story, Alex and his friends discovered an ancient treasure in Whispering Hollow and decided to donate the artifacts to the local museum.\n\n"
        "In the past, did you have a similar experience where you found something valuable or interesting? Tell the story. Describe what you found, what you did with it, and how you felt about your decision.\n\n"
        "Remember to use past tense in your writing.\n\n"
        "<b>A student's sample answer:</b>\n"
        "<blockquote>When I was 10, I find an old coin in my backyard. I kept it for a while and shows it to my friends. They was impressed and say it might be valuable. Later, I take it to a local antique shop, and the owner told me it was very old. I decided to give it to the museum in my town. The museum was happy and put it on display. I feel proud of my decision.<br><br><i>Copy and paste to try.</i></blockquote>"
    )
)

# Launch the interface
interface.launch()